Wearable and implantable biosensors: mechanisms and applications in closed-loop therapeutic systems.
Zeyuan ZhengRunjin ZhuIan PengZitong XuYuanwen JiangPublished in: Journal of materials chemistry. B (2024)
This review article examines the current state of wearable and implantable biosensors, offering an overview of their biosensing mechanisms and applications. We also delve into integrating these biosensors with therapeutic systems, discussing their operational principles and incorporation into closed-loop devices. Biosensing strategies are broadly categorized into chemical sensing for biomarker detection, physical sensing for monitoring physiological conditions such as pressure and temperature, and electrophysiological sensing for capturing bioelectrical activities. The discussion extends to recent developments in drug delivery and electrical stimulation devices to highlight their significant role in closed-loop therapy. By integrating with therapeutic devices, biosensors enable the modulation of treatment regimens based on real-time physiological data. This capability enhances the patient-specificity of medical interventions, an essential aspect of personalized healthcare. Recent innovations in integrating biosensors and therapeutic devices have led to the introduction of closed-loop wearable and implantable systems capable of achieving previously unattainable therapeutic outcomes. These technologies represent a significant leap towards dynamic, adaptive therapies that respond in real-time to patients' physiological states, offering a level of accuracy and effectiveness that is particularly beneficial for managing chronic conditions. This review also addresses the challenges associated with biosensor technologies. We also explore the prospects of these technologies to address their potential to transform disease management with more targeted and personalized treatment solutions.
Keyphrases
- label free
- healthcare
- drug delivery
- end stage renal disease
- systematic review
- randomized controlled trial
- physical activity
- heart rate
- spinal cord injury
- newly diagnosed
- type diabetes
- body composition
- machine learning
- mesenchymal stem cells
- electronic health record
- magnetic resonance imaging
- mental health
- insulin resistance
- case report
- metabolic syndrome
- prognostic factors
- magnetic resonance
- peritoneal dialysis
- patient reported outcomes
- current status
- deep learning
- structural basis